Approaches to the Selection of Relevant Concepts in the Case of Noisy Data
Concept lattices built on noisy data tend to be large and hence hard to interpret. We introduce several measures that can be used in selecting relevant concepts and discuss how they can be combined together. We study their performance in a series of experiments.
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